Local variability vector for text-independent speaker verification

Abstract

Total variability modeling has shown to be effective for text-independent speaker verification task. It provisions a tractable way to estimate the so-called i-vector, which describes the speaker and session variability rendered in an utterance. Due to the low dimensionality of the i-vector, channel compensation techniques such as linear discriminant analysis (LDA) and probabilistic LDA can be applied for the purpose of channel compensation. This paper proposes the local variability modeling technique, the central idea of which is to capture the local variability associated with individual dimension of the acoustic space. We analyze the latent structure associated with both the i-vector and local variability vector and show that the two representations complement each other based on the experiment conducted on NIST SRE'08 and SRE'10 datasets.